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BigDataEurope - Empowering Communities with Data Technologies


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Presentation: BigDataEurope, by Martin Kaltenböck, Semantic Web Company (Austria), at the European Data Economy Workshop taking place back to back to SEMANTiCS2015 on 15 September 2015 in Vienna

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BigDataEurope - Empowering Communities with Data Technologies

  1. 1. BIG DATA EUROPE HTTP://WWW.BIG-DATA-EUROPE.EU/ Integrating Big Data, Software & Communities for Addressing Europe’s Societal Challenges European Data Economy Workshop, Focus: Data Value Chain & Big Data & Open Data 15 September 2015, University of Economics Vienna
  2. 2. Semantic Web Company (SWC) SWC was founded 2001, head-quartered in Vienna 25 experts in linked data technologies Product: PoolParty Semantic Suite (launched 2009) Serving customers from all over the world EU- & US-based consulting services
  3. 3. Semantic Web Company (SWC) Some of our Customers ● Credit Suisse ● Boehringer Ingelheim ● Roche ● Wolters Kluwer ● BMJ Publishing Group ● Red Bull Media House ● Canadian Broadcasting Corporation (CBC) ● Pearson ● Council of the EU ● DG Environment, EC ● Healthdirect Australia ● Ministry of Finance (Austria) ● World Bank Group ● Inter-American Development Bank (IADB) ● International Atomic Energy Agency (IAEA) ● Buildings Performance Institute Europe (BPIE) ● Renewable Energy & Energy Efficiency P (REEEP) ● Global Buildings Performance Network (GBPN) ● American Physical Society Finance / Automotive / Publisher / Health Care / Public Administration / Energy / Education Selected Partners ● EBCONT ● EPAM Systems ● iQuest ● PwC ● Tenforce ● OpenLink Software ● Ontotext ● MarkLogic ● Gravity Zero ● Altotech ● Wolters Kluwer ● Taxonomy Strategies ● Digirati ● Fraunhofer (IAIS) ● University of Leipzig (INFAI) We all have one goal in mind: Make machines smart enough so that they can help us to find those needles in the haystack, which are really relevant to us.
  4. 4. The Motivation – Big Data Every day, we create 2.5 quintillion bytes of data — so much that 90% of the data in the world today has been created in the last two years alone. This data comes from everywhere: sensors used to gather climate information, posts to social media sites, digital pictures and videos, purchase transaction records, and cell phone GPS signals to name a few. This data is big data. Source:
  5. 5. The Motivation – Big Data BIG DATA
  6. 6. Big Data Dimensions Volume Velocity Variety 100010101010101010101010 010100101010101010010100 101010010100101010010100 101001010100101010100101 010001010101010010101010 101010101001010101010101 001010101110001010101010 101010101001010010101010 101001010010101001010010 101001010010100101010010 101010010101000101010101 00101010101 1000101010 1010101010 1010010100 1010101010 1001010010 ……. …………. …… ………..……. . …………… 1 0 1000101010 1010101010 1010010100 1010101010 100101oo11 Veracity!
  7. 7. Big Data Dimensions
  8. 8. Big Data in Europe: Challenges, Opportunities Health Climate Energy Transport Food Societies Security Lorem ipsum dolors KSDJO PSCKK SDKAB LKASJL LAWWD S wpweppe pwpisio we 10101 00110 10100 10101 0 Regional Data Repositories 10101 00110 10100 10101 0 10101 00110 10100 10101 0 #2: Interlink, Centralise Access, Explore 1010101001010101010 0101101000101010101 0010101010100001011 0100010101010100101 0101010010010101010 0101010101001011010 001010 Data Eleme nt #3: Analyse, Discover, Visualize #4: Mashup, Cross-domain Exploitation Journalists Authorities
  9. 9. Big Data in Europe: Obstacles 30-sept.-15 #1 Big Data “Variety“ problem  Multiple Data Sources  Required: Integration, Harmonisation #2 Opening-up Data concerns  Loss of control, lack of tracking  Reservations about large corporations #3 Limited Skills, Training, Technology  Lack of Data Scientists  Lack of Generic Architectures, components
  10. 10. Big Data in Europe: Obstacles 30-sept.-15 Extraction, Curation Quality, Linking, Integration Publication, Visualization, Analysis Extraction, Curation, Quality, Linking, Integration, Publication, Visualization, Analysis Health Transport Security Extraction Curation Quality Linking Integration Publication Visualization Analysis Data Repositories Linked Open Data Cloud Stage 1 Stage 2 Stage 3 Food SocietiesClimate Energy
  11. 11. BDE Partners
  12. 12. Rationale  Show societal value of Big Data  Lower barrrier for using big data technologies o Required effort and resources o Limited data science skills  Help establishing cross- lingual/organizational/domain Data Value Chains 30-sept.-15
  13. 13. Rationale COORDINATION Stakeholder Engagement (Requirements Elicitation) SUPPORT Design, Realise, Evaluate Big Data Aggregator Platform Create and Manage Societal Big Data Interest Groups Cloud-deployment ready Big Data Aggregator Platform CSA Measures Results
  14. 14. Summary Two clearly defined coordination and support measures:  Coordination: Engaging with a diverse range of stakeholder groups representing particularly the Horizon 2020 societal challenges Health, Food & Agriculture, Energy, Transport, Climate, Social Sciences and Security; Collecting requirements for the ICT infrastructure needed by data- intensive science practitioners tackling a wide range of societal challenges; covering all aspects of publishing and consuming semantically interoperable, large-scale data and knowledge assets;  Support: Designing, realizing and evaluating a Big Data Aggregator platform infrastructure that meets requirements, minimises disruption to current workflows, and maximises the opportunities to take advantage of the latest European RTD developments (incl. multilingual data harvesting, data analytics & visualisation). BigDataEurope will implement and apply two main instruments to successfully realize these measures:  Build Societal Big Data Interest/Community Groups in the W3C interest group scheme & involving a large number of stakeholders from the Horizon 2020 societal challenges as well as technical Big Data experts;  Design, integrate and deploy a cloud-deployment-ready Big Data aggregator platform
  15. 15. Orthogonal Dimensions of Big Data Ecosystems Generic Big Data Enabling Technologies Data Value Chain Data Generation & Acquisition Data Analysis & Processing Data Storage & Curation Data Visualization & Usage Data-driven Services SocietalChallenges DomainSpecificDataAssets&Technology Healthcare Food Security Energy Intelligent Transport Climate & Environment Inclusive & Reflective Societies Secure Societies
  16. 16. BDE Stakeholder Engagement Approach & Activities BDE Community Tools – JOIN IN NOW ! • Website: news, events, community, … • 7 x BDE W3C Community Groups • 7+1x Mailing Lists • 7 x SC Workshops/Year = 21 Workshops • Full set of communication tool-set… Future Outlook • BDE Aggregator Platform • For download / internal use • Cloud Version • Big Data Technology Support Tools
  17. 17. Domains, Focus Areas & Data Assets Societal Domain Preliminary Big Data Focus area Selected Key Data assets Life Sciences & Health Heterogeneous data Linking & integration Biomedical Semantic Indexing & QA ACD Labs / ChemSpider, ChEBI, ChEMBL, Con-ceptWiki, DrugBank, EN- ZYME, Gene Ontology, GO Annotation, Swis-sProt, UniProt, Wik-iPathways, PubMed, MeSH, Disease Ontology (DO), Joint Chemical Dic-tionary (Jochem), Bio-ASQ datasets Food & Agriculture Large-scale distributed data integration INFOODS, AQUASTAT Green Learning Network (GLN), Agricultural Bibliography Network (ABN), AGRIS, AquaMaps, Fishbase Energy Real-time monitoring, stream processing, data analytics, and decision support European Energy Exchange Data, smart meter measurement data, gas/fuels/energy market/price data, consumption statistics, equipment condition monitoring data) Transport Streaming sensor network & geo- spatial data integration GTFS data, OSM/ LinkedGeoData, MobilityMaps, Transport sensor data, ROSATTE Road safety attributes, European Road Data Infrastructure - EuroRoadS Climate Real-time monitoring, stream processing, and data analytics. European Grid Infrastructure (EGI), Databases hosting atmospheric data. Several software frameworks for simulation, calibration and reconstruction. Social Sciences Statistical and research data linking & integration Federated social sciences data catalogs, statistical data from public data portals and statistical offices (e.g. EuroStats, UNESCO, WorldBank) Security Real-time monitoring, stream processing, and data analytics. Image data analysis Earth Observation data (e.g. Very High Resolution Satellite Imagery acquired from commercial providers and governmental systems) and collateral data for supporting CFSP/CSDP missions and operations, Databases hosting atmospheric Data. Experimental and simulation data concerning dispersion
  18. 18. Work Packages & Implementation Phases Community Building M1-M12 M13-M24 M25-M36 Enabling Technologies Component Integration Uptake Integrator Deployment Community Assessment WP3 – Big Data Generic Enabling Technologies & Architecture WP5 – Big Data Integrator Instances WP7 – Dissemination & Communication WP2 – Community Building & Requirements WP4 – Big Data Integrator Platform WP6 – Real-life Deployment & User Evaluation
  19. 19. Blueprint of the Data Aggregator Platform Batch Layer Speed Layer Data Storage Real-time data & Transactions … Batch View Real-time View messagepassing message passing Applications & Showcases Real-time dashboards Domain-specific BDE apps Big Data Analytics In-stream Mining BDEPlatform& Intelligence Input data Stream Spatial Social Statistical Temporal Transaction al Imagery + Semantic Layer (Retaining Semantics using LD Lambda Architecture
  20. 20. Announcements…. Workshop SC2 (Agriculture & Food): 22.9.2015, Paris, INFOS Workshop SC7 (Secure Societies): 30.9.2015, Brussels, INFO Workshop SC4 (Transport): .10.2015, Bordeaux, INFO Workshop SC6 (Social Science): 18.11.2015, Luxembourg, INFO
  21. 21. Martin Kaltenböck, Semantic Web Company GmbH Mariahilfer Strasse 70/8, A-1070 Vienna +43-1-4021235 Your Questions please…. 30-sept.-15 #BigDataEurope